AIRS Data Assimilation at SPoRT Brad Zavodsky and Will McCarty (UAH) - - PowerPoint PPT Presentation

1 transitioning unique NASA data and research technologies to the NWS

AIRS Data Assimilation at SPoRT

Brad Zavodsky and Will McCarty (UAH) Shih-hung Chou and Gary Jedlovec (MSFC)

AIRS Science Team Meeting Greenbelt, MD October 10, 2007

2 transitioning unique NASA data and research technologies to the NWS

Motivation: Use of AIRS measurements within a data assimilation system can potentially provide better atmospheric representation—particularly over data void regions—and improve short-term weather forecasts

♦ SPoRT AIRS Assimilation focuses on short-term regional

forecasts—compliments work at JCSDA

♦ Profile Assimilation (B. Zavodsky)

• Motivation and review of previous case study work
• Design of experiment for month-long statistics
• Results from month-long statistics

♦ Direct Radiance Assimilation (W. McCarty)

• Channel selection and assimilation cycle
• Results of case study

♦ SPoRT AIRS DA work presented Sept. 24 and 25 at EUMETSAT/AMS

Satellite Conference in Amsterdam, The Netherlands

Outline

3 transitioning unique NASA data and research technologies to the NWS ♦ Assimilation of AIRS profiles may benefit regional centers that are influenced by

data sparse areas but are not equipped to handle radiance assimilation

• Melbourne and Miami NWS WFOs

♦ Previous work at SPoRT has focused on Nov. 20-22, 2005 case study

• Found that AIRS profiles have positive impact on analyses by shifting large-scale

model first-guess towards rawinsonde observations

• AIRS-updated initial conditions showed positive impact in temperature, mixing ratio,

and 6-hr cumulative precipitation at most forecast times

♦ More days needed to be run to find new case studies and to obtain a more

robust set of cumulative statistics of forecast impact

• 33 days of model runs from 17 January to 22 February 2007 were run (missing initial

conditions for 3-5 February and 11 February)

• These results are shown herein

Profile Assimilation Introduction

4 transitioning unique NASA data and research technologies to the NWS ♦ L2 Version 5 temperature and moisture profiles

assimilated over land and water with quality control using PBest value in each profile

• Eastern and central CONUS swathes combined into one

swath; assimilation time is mean of the two overpasses

• Only night time overpasses used

♦ 12-km WRF initialized at 0000 UTC on each forecast

date using 40-km ETA/NAM; ADAS to assimilate profiles

Experiment Design

Valid: 0836-0848 UTC Valid: 0700-0712 UTC AIRS Time: AIRS Time: 0800 UTC 0800 UTC

♦ Results of the 33 days of model runs are validated

using sensible weather parameters compared to

• bservations
• Temperature and mixing ratio verified with 50

radiosondes east of 105oW

• 6-hr cumulative precipitation verified with NCEP Stage

IV data east of 105oW mapped to WRF grid

5 transitioning unique NASA data and research technologies to the NWS ♦ AIRS reduces temperature bias at most levels

by ≈0.3oC in lower and upper levels

♦ AIRS changes low and mid-level moisture by

as much as 5% at some levels

♦ Temperature and moisture adjustments made

without large increases to RMS error

Results: 36 Hour Forecast Impact

♦ 6-hr cumulative precipitation improves with

inclusion of AIRS profiles

• Larger ETS (bars) for AIRS runs indicates

improvement in predicted precipitation location and amount

• Bias scores (lines) closer to 1.0 for AIRS

suggest improvement in coverage of precipitation features

0.05 0.1 0.15 0.2 0.25 0.3 0.254 2.540 6.370 12.70 19.05 0.25 0.5 0.75 1 1.25 1.5

Equitable Threat Score Bias Score Minimum Precipitation Threshold (mm)

CNTL AIRS

6 transitioning unique NASA data and research technologies to the NWS

Motivation: Use of AIRS measurements within a data assimilation system can potentially provide better atmospheric representation—particularly over data void regions—and improve short-term weather forecasts

♦ SPoRT AIRS Assimilation focuses on short-term regional

forecasts—compliments work at JCSDA

♦ Profile Assimilation (B. Zavodsky)

• Motivation and review of previous work
• Design of experiment for month-long statistics
• Results from month-long statistics

♦ Direct Radiance Assimilation (W. McCarty)

• Channel selection and assimilation cycle
• Results of case study

♦ SPoRT AIRS DA work presented Sept. 24 and 25 at EUMETSAT/AMS

Satellite Conference in Amsterdam, The Netherlands

Outline

7 transitioning unique NASA data and research technologies to the NWS ♦ In the NCEP Global Data Assimilation System (GDAS), AIRS has already

been shown to have a significant impact in both northern and southern hemisphere global forecasts (Le Marshall et al. 2006)

♦ Previous work focused on preparation of AIRS radiances for data

assimilation

• CO2 Sorting Technique can detect clouds and determine uncontaminated

channels in hyperspectral data to increase the number of usable channels over a masking approach

♦ The proper use and assessment of these measurements within a regional

system—such as the North American Model (NAM) Data Assimilation System (NDAS)—has yet to be fully assessed

• Considerations of the proper utilization of AIRS data within the pseudo-
• perational NDAS environment and a preliminary look at their impact are

investigated herein

Radiance Assimilation Introduction

8 transitioning unique NASA data and research technologies to the NWS ♦ Operationally, NCEP GFS uses 151

channels of the 281 channel subset

♦ Limitations to using a regional model:

• lower Ptop (2 hPa; red line)
• O3 not used in regional model

♦ No shortwave (< 5 µm) channels are used ♦ Plots show profile normalized Jacobians of

each constituent:

♦ Green hashes denote 151 GDAS channels ♦ Red hashes denote 103 regional channels ♦ No additional channels in regional subset

that are not used in global analysis

Channel Selection for Regional Assimilation

Pressure (hPa)

• 0 +

T q O3

i q i dq b dT 1 . *

9 transitioning unique NASA data and research technologies to the NWS ♦ All NCEP operational observations are assimilated every 3 hours (± 1.5 hrs) for the

NOAIRS runs; AIRS radiances are the only difference between NOAIRS and AIRS runs

♦ A two-week spin-up period to propagate the impact of the AIRS measurements through

the analysis and allow bias corrections to stabilize

♦ Gridpoint Statistical Interpolation (GSI) and the Weather Research and Forecasting

Nonhydrostatic Mesoscale Model (WRF-NMM) used as analysis and model systems

Assimilation Cycle

48hr 00 06 12 18 00 03 09 15 21 48hr 48hr 48hr Time (UTC) 48hr

10 transitioning unique NASA data and research technologies to the NWS

Initial Results

Analysis AIRS NOAIRS

B A

♦ 48-hr forecast valid at 0000 UTC on 11 April

2007

♦ 500 hPa height anomalies for control (NOAIRS;

blue) and control+AIRS (AIRS; red); corresponding NDAS analysis in black

♦ Solid contours correspond to troughs; dashed

contours correspond to ridges

Pressure (hPa) Height Anomaly A B R σ

♦ A: model domain (dashed lines) ♦ B: subdomain characterisized by

conventional obs in analysis (solid lines)

♦ Both height anomaly correlation (R)

and standard deviation (σ) show significant improvement throughout the troposphere

11 transitioning unique NASA data and research technologies to the NWS ♦ SPoRT AIRS Assimilation focuses on short-term regional forecasts—compliments

work at JCSDA

♦ Profile Assimilation Conclusions/Future Work

• For 33 days of model runs in late Jan./early Feb.
• Biases are reduced in temperature and mixing ratio at most levels
• 6-hr cumulative precipitation coverage and forecast accuracy improve
• Further analysis of individual days from case study to determine where AIRS provides

most added value; migrate to 3DVAR

♦ Direct Radiance Assimilation Conclusions/Future Work

• Limitations in use of AIRS radiances in regional NDAS reduced the number of usable

channels by 17% relative to the 281 subset but still retained 37% of the channels overall

• An initial case study shows statistically significant forecast improvement throughout the

entire model domain due to the assimilation of AIRS data

• Further investigate determination of cloud contamination using CO2 sorting technique;

further investigate use of AIRS radiances over a longer set of studies

Summary

12 transitioning unique NASA data and research technologies to the NWS

Questions? Suggestions? Comments?